Previous studies in SER research have shown that spectral and prosodic characteristics of speech contain emotion-related information. Spectral features include lower formants frequencies (F1 and F2), speech amplitude and energy, zero crossing rate (ZCR) and spectral parameters, e.g., like spectral flux and spectral roll-off [1]}, [2]}, [3]}. Prosodic features include pitch, pitch harmonics, intonation, and speaking rate [2]}, [3]}. The acoustic front-ends are used with Gaussian mixture model (GMM) or support vector machines (SVMs) as back-end classifiers for SER tasks [6]}. Studies with prosody reveal that high arousal emotions, such as Angry, Happy and Fear, have higher average pitch values with abrupt pitch variations whereas low arousal emotions like Sadness and Neutral have lower pitch values with consistent pitch contours [7]}, [8]}, [9]}, [10]}, [11]}. The authors in [12]} have reported that recognition accuracy of Anger is higher near F2 (1250-1750 Hz) and that of Neutral is higher near F1 (around 200-1000 Hz). Authors in [13]} report that center frequencies of F2 and F3 are reduced in depressed individuals. In [14]}, the authors report that high arousal emotions have higher mean F1 and lower F2 and high (positive) valence emotions have high mean F2. In [15]}, authors report discrimination between idle and negative emotions using temporal patterns in formants. In [16]}, the authors have demonstrated that non-linear frequency scales, such as logarithmic, mel and equivalent rectangular bandwidth (ERB), have considerable impact in SER performance over linear frequency scale.
